Vehicular fog computing (VFC) is a growing approach for delivering low-latency services to IoT devices and intelligent traffic systems. In VFC, vehicles and roadside units (RSUs) collaborate to form a fog infrastructure, processing and storing real-time traffic data at the network edges. However, vehicle mobility creates challenges in maintaining stable communication and network connectivity. Differences in distance, location, speed, and direction between vehicles and infrastructure can disrupt vehicle-to-infrastructure (V2I) communication, impacting reliability. To address these challenges, we explored dynamic fog formation using mobile vehicles and RSUs in city scenarios. We focus on real-time RSU association and fog formation to prevent outdated information from causing failures in V2I communication. We propose an approach utilizing deep reinforcement learning (DRL), particularly the dueling double deep Q-network (Dueling DDQN) algorithm. Through simulations using the Constant Speed Mobility (CSM) model, we compared its performance with DQN, DDQN, and Dueling DQN. Results show that Dueling DDQN outperforms the other methods, effectively improving the freshness of real-time system information.

Multi-access Edge Computing (MEC) and Vehicular-Fogs (VFs) are placed nearer to user equipment (UE), reducing propagation latency compared to traditional cloud-based systems and ensuring a high standard of Quality of Service (QoS). Nevertheless, MEC sites can become congested and overloaded during peak traffic periods, such as concerts or sporting events. To address this, offloading techniques can shift intensive computational tasks from devices with limited resources to those with greater capacity, enhancing task performance and thereby increasing battery longevity. This study investigates the offloading within a two-tier framework of MEC and VF, focusing on the offloading of MEC to VF. Maintaining QoS is challenging due to the instability of fog networks caused by high-speed vehicle movement, which disrupts both vehicle-to-vehicle and vehicle-to-infrastructure communications. To mitigate this, we analyze vehicle mobility using a Gauss-Markov Mobility (GMM) model. Our main goal is to reduce the average system cost by optimizing both latency and energy consumption while accounting for vehicle mobility. We approach this challenge as a multi-objective optimization problem and develop a reinforcement learning environment. Additionally, we propose an algorithm based on imitation learning called Weighted-Ratio Based TD3 (WRTD3), an enhancement of the TD3 algorithm, to effectively manage these complexities.

In real-time status update services for the Internet of Things (IoT), the timely dissemination of information requiring timely updates is crucial to maintaining its relevance. Failing to keep up with these updates results in outdated information. The Age of Information (AoI) serves as a metric to quantify the freshness of information. The Existing works to optimize AoI primarily focus on the transmission time from the information source to the monitor, neglecting the transmission time from the monitor to the destination. This oversight significantly impacts information freshness and subsequently affects decision-making accuracy. To address this gap, we designed an edge-enabled vehicular fog system to lighten the computational burden on IoT devices. We examined how information transmission and request-response times influence end-to-end AoI. As a solution, we proposed dueling-deep queue network (dueling-DQN), a deep reinforcement learning (DRL)-based algorithm, and compared its performance with the DQN policy and analytical results. Our simulation results demonstrate that the proposed dueling-DQN algorithm outperforms both DQN and analytical methods, highlighting its effectiveness in improving real-time system information freshness. Considering the complete end-to-end transmission process, our optimization approach can improve decision-making performance and overall system efficiency.

With the dynamic demands and stringent requirements of various applications, networks need to be high-performance, scalable, and adaptive to changes. Researchers and industries view network softwarization as the best enabler for the evolution of networking to tackle current and prospective challenges. Network softwarization must provide programmability and flexibility to network infrastructures and allow agile management, along with higher control for operators. While satisfying the demands and requirements of network services, energy cannot be overlooked, considering the effects on the sustainability of the environment and business. This paper discusses energy efficiency in modern and future networks with three network softwarization technologies: SDN, NFV, and NS, introduced in an energy-oriented context. With that framework in mind, we review the literature based on network scenarios, control/MANO layers, and energy-efficiency strategies. Following that, we compare the references regarding approach, evaluation method, criterion, and metric attributes to demonstrate the state-of-the-art. Last, we analyze the classified literature, summarize lessons learned, and present ten essential concerns to open discussions about future research opportunities on energy-efficient softwarized networks.

The emergence of 5G networks has enabled the deployment of a two-tier edge and vehicular-fog network. It comprises Multi-access Edge Computing (MEC) and Vehicular-Fogs (VFs), strategically positioned closer to Internet of Things (IoT) devices, reducing propagation latency compared to cloud-based solutions and ensuring satisfactory quality of service (QoS). However, during high-traffic events like concerts or athletic contests, MEC sites may face congestion and become overloaded. Utilizing offloading techniques, we can transfer computationally intensive tasks from resource-constrained devices to those with sufficient capacity, for accelerating tasks and extending device battery life. In this research, we consider offloading within a two-tier MEC and VF architecture, involving offloading from MEC to MEC and from MEC to VF. The primary objective is to minimize the average system cost, considering both latency and energy consumption. To achieve this goal, we formulate a multi-objective optimization problem aimed at minimizing latency and energy while considering given resource constraints. To facilitate decision-making for nearly optimal computational offloading, we design an equivalent reinforcement learning environment that accurately represents the network architecture and the formulated problem. To accomplish this, we propose a Distributed-TD3 (DTD3) approach, which builds on the TD3 algorithm. Extensive simulations, demonstrate that our strategy achieves faster convergence and higher efficiency compared to other benchmark solutions.

The federation between cloud and edge has been proposed to exploit the advantages of both technologies. However, the existing studies have only considered cloud-edge computing systems which merely support vertical offloading from edges to clouds in one direction. However, there are certain cases, where the offloading needs to be done from clouds to edges and between edges. Such a cloud to edge offloading is called reverse offloading. To this end, this paper proposes a generic Omni-directional architecture of cloud-edge computing systems intending to provide vertical and horizontal offloading. To investigate the effectiveness of the proposed architecture in different operational scenarios, we formulate the dual cost optimization problem with different latency (loose, low, ultra-low) constraints. We develop an offloading algorithm using simulated annealing (SA). The experimental results show by our proposed OMNI architecture we can reduce the total cost by 15-25% and 10-20% in non-uniform and uniform inputs, respectively, compared to other existing architectures. The average latency in OMNI architecture is relatively very less compared to other architectures. It also increases utilization in the edge nodes by 5-30% in comparison to other existing architectures.

Vehicular-fog system consists of vehicles with computing resources that are mostly under-utilized. Therefore, an edge system may offload some workloads for remote execution at nearby vehicular- fogs. Whether this is cost-effective depends on not only the costs and computation capacities of vehicles but also the amount of workloads and associated latency constraint. In this paper, we consider a two-tier federated Edge and Vehicular- Fog (EVF) architecture and aim to minimize overall cost while meeting latency constraint by setting up an appropriate offloading configuration. We model this to a singleobjective mixed integer programming problem. To solve this mixed integer problem in real time we propose an iterative greedy algorithm using the queuing model. The results show, our proposed architecture reduces the cost of vehicular-fogs by 40â€"45% and the total cost by 35â€"40% compared to the existing architecture and help the edge to provide services beyond its capacity with specified latency constraint.

Network function virtualization (NFV), with its virtualization technologies, brings cloud computing to networking. Virtualized network functions (VNFs) are chained together to provide the required functionality at runtime on demand. It has a direct impact on power consumption depending on where and how these VNFs are placed and chained to accomplish certain demands as the power consumption of a physical machine (PM) depends on its traffic load. One of the advantages of VNF placement over traditional virtual machine placement is that virtualization is not limited solely to servers. The PMs, including the servers and varying loads to these machines and their utilization, are critical issues related to the network's energy consumption. In this paper, we designed a dynamic energy-saving model with NFV technology using an M/M/c queuing network with the minimum capacity policy where a certain amount of load is required to start the machine, which increases the utilization of the machine and avoids frequent changes of the machines' states. We formulate an energy-cost optimization problem with capacity and delay as constraints. We propose a dynamic placement of VNF chains (DPVC) heuristic solution to the NP-hard problem. The results show that the DPVC solution performs better and saves more energy. It uses 45%-55% less active nodes to satisfy the requested demands and increases the utilization of the active nodes by 40%-50% compared to other algorithms.

Due to the large installed base of distributed legacy networks, software defined networking (SDN) nodes may be required to coexist with legacy nodes to form hybrid networks. It has been shown that such a hybrid network has better performance than a pure legacy network due to smarter transmission scheduling. Despite such advantages, limited budgets continue to hinder the rapid adaptation of SDNs. Only a part of the network can be upgraded at a time especially for large-scale networks. In this paper, we define the minimum percentage of SDN nodes in a path, and paths with at least one SDN node, as the hop coverage and path coverage, respectively. We intend to evaluate the relationship between cost and coverage in the partially deployed SDNs. We formulate SDN node selection as four optimization problems with hop/path coverage and cost as objectives and constraints, respectively, and vice-versa. We propose two heuristic solutions: 1) maximum number of uncovered path first (MUcPF) and 2) maximum number of minimum hop covered path first (MMHcPF), to these NP-hard problems. Through a MATLAB experiment, we show that MUcPF is significantly better in terms of economy and efficiency to establish a hybrid path between every pair of hosts in the network. In particular, it required 5%-15% less investment to achieve 100% path coverage compared to other algorithms. The results show the coverage consistency of MMHcPF on each individual path along with gains in terms of cost and efficiency. It takes 5%-20% less investment to achieve certain hop coverage target compared to other existing algorithms.